Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
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Abstract (arXiv)
Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is identifiable only up to an observational equivalence class, each equivalence class is characterized by a unique sparsest DAG. Recovering the sparsest DAG from finite samples, however, remains difficult. Although existing methods are asymptotically consistent, they do not provide an explicit finite-sample procedure for recovering the unique sparsest DAG, nor do they handle models with an arbitrary number of latent confounders. In this paper, we propose a finite-sample method for recovering the sparsest DAG without imposing any restriction on the number of latent confounders. Simulation studies and real-data analyses demonstrate that the proposed method achieves superior finite-sample performance compared with existing approaches.
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References · 5
- 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
- 2013ParceLiNGAM: A causal ordering method robust against latent confounderscited by 29
- 2023Identification of Causal Structure with Latent Variables Based on Higher Order Cumulantscited by 8
- 2024Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confoundingcited by 6
- 2025Identification of Causal Direction under an Arbitrary Number of Latent Confounderscited by 1
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